Least-squares reverse time migration in visco-acoustic media based on symplectic stereo-modeling method

The authors proposed a symplectic stereo-modeling method(SSM)in the Birkhoffian dynam-ics and apply it to the visco-acoustic least-squares reverse time migration(LSRTM).The SSM adopts ste-reo-modeling operator in space and symplectic Runge-Kutta scheme in time,resulting in great ability in suppressing numerical dispersion and long-time computing.These advantages are further confirmed by numerical dispersion analysis,long-time computation test and computational efficiency comparison.After these theoretical analyses and experiments,acoustic and visco-acoustic LSRTM are tested and compared between SSM method and the conventional symplectic method(CSM)using the fault and marmousi models.Meanwhile,dynamic source encoding and exponential decay moving average gradients method are adopted to reduce the computation cost and improve the convergence rate.The imaging results show that LSRTM based on visco-acoustic wave equations effectively takes into account the influence of viscosity can therefore compensate for the amplitude attenuation.Besides,SSM method not only has high numerical accuracy and computational efficiency,but also performs effectively in LSRTM.

Regularized least-squares migration of simultaneous-source seismic data with adaptive singular spectrum analysis

Simultaneous-source acquisition has been recog- nized as an economic and efficient acquisition method, but the direct imaging of the simultaneous-source data produces migration artifacts because of the interference of adjacent sources. To overcome this problem, we propose the regularized least-squares reverse time migration method （RLSRTM） using the singular spectrum analysis technique that imposes sparseness constraints on the inverted model. Additionally, the difference spectrum theory of singular values is presented so that RLSRTM can be implemented adaptively to eliminate the migration artifacts. With numerical tests on a fiat layer model and a Marmousi model, we validate the superior imaging quality, efficiency and convergence of RLSRTM compared with LSRTM when dealing with simultaneoussource data, incomplete data and noisy data.

Multisource least-squares reverse-time migration with structure-oriented filtering

The technology of simultaneous-source acquisition of seismic data excited by several sources can significantly improve the data collection efficiency. However, direct imaging of simultaneous-source data or blended data may introduce crosstalk noise and affect the imaging quality. To address this problem, we introduce a structure-oriented filtering operator as preconditioner into the multisource least-squares reverse-time migration （LSRTM）. The structure-oriented filtering operator is a nonstationary filter along structural trends that suppresses crosstalk noise while maintaining structural information. The proposed method uses the conjugate-gradient method to minimize the mismatch between predicted and observed data, while effectively attenuating the interference noise caused by exciting several sources simultaneously. Numerical experiments using synthetic data suggest that the proposed method can suppress the crosstalk noise and produce highly accurate images.

Multiparameter least-squares reverse time migration for acoustic–elastic coupling media based on ocean bottom cable data

In marine seismic exploration,ocean bottom cable technology can record multicomponent seismic data for multiparameter inversion and imaging.This study proposes an elastic multiparameter lease-squares reverse time migration based on the ocean bottom cable technology.Herein,the wavefield continuation operators are mixed equations:the acoustic wave equations are used to calculate seismic wave propagation in the seawater medium,whereas in the solid media below the seabed,the wavefields are obtained by P-and S-wave separated vector elastic wave equations.At the seabed interface,acoustic–elastic coupling control equations are used to combine the two types of equations.P-and S-wave separated elastic migration operators,demigration operators,and gradient equations are derived to realize the elastic least-squares reverse time migration based on the P-and S-wave mode separation.The model tests verify that the proposed method can obtain high-quality images in both the P-and S-velocity components.In comparison with the traditional elastic least-squares reverse time migration method,the proposed method can readily suppress imaging crosstalk noise from multiparameter coupling.

Super-resolution least-squares prestack Kirchhoff depth migration using the L_0-norm

Least-squares migration （LSM） is applied to image subsurface structures and lithology by minimizing the objective function of the observed seismic and reverse-time migration residual data of various underground reflectivity models. LSM reduces the migration artifacts, enhances the spatial resolution of the migrated images, and yields a more accurate subsurface reflectivity distribution than that of standard migration. The introduction of regularization constraints effectively improves the stability of the least-squares offset. The commonly used regularization terms are based on the L2-norm, which smooths the migration results, e.g., by smearing the reflectivities, while providing stability. However, in exploration geophysics, reflection structures based on velocity and density are generally observed to be discontinuous in depth, illustrating sparse reflectance. To obtain a sparse migration profile, we propose the super-resolution least-squares Kirchhoff prestack depth migration by solving the L0-norm-constrained optimization problem. Additionally, we introduce a two-stage iterative soft and hard thresholding algorithm to retrieve the super-resolution reflectivity distribution. Further, the proposed algorithm is applied to complex synthetic data. Furthermore, the sensitivity of the proposed algorithm to noise and the dominant frequency of the source wavelet was evaluated. Finally, we conclude that the proposed method improves the spatial resolution and achieves impulse-like reflectivity distribution and can be applied to structural interpretations and complex subsurface imaging.

Least-squares reverse time migration method using the factorization of the Hessian matrix

Least-squares reverse time migration(LSRTM)can eliminate imaging artifacts in an iterative way based on the concept of inversion,and it can restore imaging amplitude step by step.LSRTM can provide a high-resolution migration section and can be applied to irregular and poor-quality seismic data and achieve good results.Steeply dipping refl ectors and complex faults are imaged by using wavefi eld extrapolation based on a two-way wave equation.However,the high computational cost limits the method’s application in practice.A fast approach to realize LSRTM in the imaging domain is provided in this paper to reduce the computational cost signifi cantly and enhance its computational effi ciency.The method uses the Kronecker decomposition algorithm to estimate the Hessian matrix.A low-rank matrix can be used to calculate the Kronecker factor,which involves the calculation of Green’s function at the source and receiver point.The approach also avoids the direct construction of the whole Hessian matrix.Factorization-based LSRTM calculates the production of low-rank matrices instead of repeatedly calculating migration and demigration.Unlike traditional LSRTM,factorization-based LSRTM can reduce calculation costs considerably while maintaining comparable imaging quality.While having the same imaging eff ect,factorization-based LSRTM consumes half the running time of conventional LSRTM.In this regard,the application of factorization-based LSRTM has a promising advantage in reducing the computational cost.Ambient noise caused by this method can be removed by applying a commonly used fi ltering method without signifi cantly degrading the imaging quality.

Steeply dipping structural target-oriented viscoacoustic least-squares reverse time migration and its application

Steeply dipping structural imaging is a significant challenge because surface geophones cannot obtain seismic primary reflection wave information from steeply dipping structures.Prismatic waves with a significant amount of steeply dipping information can be used to improve the imaging eff ect on steeply dipping structures.Subsurface attenuation leads to amplitude loss and phase distortion of seismic waves,and ignoring this attenuation during imaging can cause blurring of migration amplitudes.In this study,we proposed a steeply dipping structural target-oriented viscoacoustic least-squares reverse time migration(LSRTM)method with prismatic and primary waves as an objective function based on the viscous wave equation,while deriving Q-compensated wavefield propagation and joint operators of prismatic and primary waves and the Q-compensated demigration operator.Numerical examples on synthetic and field data verified the advantages of the proposed viscoacoustic LSRTM method of joint primary and prismatic waves over conventional viscoacoustic LSRTM and non-compensated LSRTM when using attenuating observed data.

LSM6 promotes cell proliferation and migration regulated by HMGB1 in laryngeal squamous cell carcinoma

Elevated levels of high mobility group protein B1(HMGB1)play a significant role in the pathogenesis of many diseases,but is particularly important for the formation of malignant tumors.Nonetheless,the function of HMGB1 and the underlying mechanism of laryngeal squamous cell carcinoma(LSCC)remain incompletely understood,causing uncertainty.Here we found immunohistochemistry from 97 LSCC tissues showed HMGB1 was upreg-ulated,which was associated with poor differentiation.HMGB1 knockdown could significantly inhibit wound closure and colony formation.The full-genome gene expression microarray was performed to investigate the mechanism.After knockdown of HMGB1 by siRNA,among the expressed differential genes,10 genes were ran-domly selected for validation.Then,shRNA lentivirus targeting these genes were constructed to explore their role in LSCC by cell proliferation assay.LSM6 downregulation was dramatically promoted by HMGB1 knockdown,resulting in higher expression in LSCC tissues.Furthermore,downregulation of LSM6 could significantly suppress cell proliferation,migration and colony formation.This study indicated that HMGB1 promoted LSCC cell malig-nant phenotypes through regulation of LSM6.We anticipate that HMGB1-LSM6 could be a putative therapeutic target for LSCC.

Huber inversion-based reverse-time migration with de-primary imaging condition and curvelet-domain sparse constraint

Least-squares reverse-time migration(LSRTM) formulates reverse-time migration(RTM) in the leastsquares inversion framework to obtain the optimal reflectivity image. It can generate images with more accurate amplitudes, higher resolution, and fewer artifacts than RTM. However, three problems still exist:(1) inversion can be dominated by strong events in the residual;(2) low-wavenumber artifacts in the gradient affect convergence speed and imaging results;(3) high-wavenumber noise is also amplified as iteration increases. To solve these three problems, we have improved LSRTM: firstly, we use Hubernorm as the objective function to emphasize the weak reflectors during the inversion;secondly, we adapt the de-primary imaging condition to remove the low-wavenumber artifacts above strong reflectors as well as the false high-wavenumber reflectors in the gradient;thirdly, we apply the L1-norm sparse constraint in the curvelet-domain as the regularization term to suppress the high-wavenumber migration noise. As the new inversion objective function contains the non-smooth L1-norm, we use a modified iterative soft thresholding(IST) method to update along the Polak-Ribie re conjugate-gradient direction by using a preconditioned non-linear conjugate-gradient(PNCG) method. The numerical examples,especially the Sigsbee2 A model, demonstrate that the Huber inversion-based RTM can generate highquality images by mitigating migration artifacts and improving the contribution of weak reflection events.

Least-squares reverse-time migration for reflectivity imaging

A least-squares reverse-time migration scheme is presented for reflectivity imaging. Based on an accurate reflection modeling formula, this scheme produces amplitude-preserved stacked reflectivity images with zero phase. Spatial preconditioning, weighting and the Barzilai-Borwein method are applied to speed up the convergence of the least-squares inversion. In addition, this scheme compensates the effect of ghost waves to broaden the bandwidth of the reflectivity images. Furthermore, roughness penalty constraint is used to regularize the inversion, which in turn stabilizes inversion and removes high-wavenumber artifacts and mitigates spatial aliasing. The examples of synthetic and field datasets demonstrate the scheme can generate zerophase reflectivity images with broader bandwidth, higher resolution, fewer artifacts and more reliable amplitudes than conventional reverse-time migration.

Plane-Wave Least-Squares Reverse Time Migration for Rugged Topography

We present a method based on least-squares reverse time migration with plane-wave encoding （P-LSRTM） for rugged topography. Instead of modifying the wave field before migration, we modify the plane-wave encoding function and fill constant velocity to the area above rugged topography in the model so that P-LSRTM can be directly performed from rugged surface in the way same to shot domain reverse time migration. In order to improve efficiency and reduce I/O （input/output） cost, the dynamic en- coding strategy and hybrid encoding strategy are implemented. Numerical test on SEG rugged topography model show that P-LSRTM can suppress migration artifacts in the migration image, and compensate am- plitude in the middle-deep part efficiently. Without data correction, P-LSRTM can produce a satisfying image of near-surface if we could get an accurate near-surface velocity model. Moreover, the pre-stack P- LSRTM is more robust than conventional RTM in the presence of migration velocity errors.

小尺度散射体稀疏最小二乘逆时偏移方法研究

地震勘探方法在深部固体矿产资源勘探中发展潜力巨大,同时也面临挑战.由于固体矿产资源地下分布呈现陡峭构造、尺度小,物性差异小的特点,常规偏移方法对小尺度矿体成像的分辨率提高有限.本文研究了一种基于稀疏促进约束的最小二乘逆时偏移方法.首先,将非均匀分布的矿体等效为随机介质,建立小尺度扰动的矿体模型;其次,改进现有最小二乘偏移方法,以稀疏模型为先验信息约束成像结果,并通过Curvelet变换压缩成像空间,经过多次迭代计算,可以提高小尺度散射体的成像分辨率;再次,对炮域记录进行随机震源编码,减少成像所需的炮集个数,通过稀疏促进约束条件,降低串扰噪声引起的成像误差.通过庐枞金属矿模型数值计算,验证本文方法可以较好的成像包含小尺度散射体的金属矿地质模型.

最小二乘Kirchhoff射线束叠前时间偏移

最小二乘偏移可以消除非规则采集、带限子波等因素对偏移结果的不利影响,提高成像剖面的分辨率和照明度,但巨大的计算成本严重制约了该方法的应用前景.本文基于地震数据的局部平面波合成/分解策略,对传统Kirchhoff时间正演/偏移的计算过程进行改进,发展了一种快速的最小二乘Kirchhoff射线束叠前时间偏移方法.同需要逐道数据映射运算的常规最小二乘Kirchhoff叠前时间偏移相比,本文方法不但具备相当的成像精度和反演收敛速度,同时由于数据的正/反向映射运算只需在稀疏的射线束中心位置进行,因此计算效率得到了大幅度的提升.文中给出的模型和实际数据算例证明了本方法的正确性和有效性.

基于构造滤波的VTI介质平面波最小二乘FFD叠前深度偏移

中国中东部地区沉积地层广泛发育,地下介质中地震波传播速度不仅与空间位置有关,还受传播方向影响,研发适用于各向异性介质的成像方法有利于提高反演成像精度。通过将VTI介质高阶傅里叶有限差分波场延拓算子纳入反演成像体系,本文实现了基于VTI介质的最小二乘FFD叠前深度偏移。针对该方法计算量大、相干成像噪声明显的不足,进行了优化:采用引入平面波编码策略,压缩地震数据,显著提高计算效率;迭代过程中提取CIG道集,在该道集上应用平面波构造滤波算子压制相干成像噪声,提高成像信噪比。基于各向异性Marmousi 2模型的数值测试结果表明,本文的优化偏移成像方法取得三点改进:(1)适用于复杂强横向变速VTI介质;(2)成像信噪比提高,效率更高;(3)克服了单程波类算法近地表成像精度低的缺陷。

Research progress on seismic imaging technology

High-precision seismic imaging is the core task of seismic exploration,guaranteeing the accuracy of geophysical and geological interpretation.With the development of seismic exploration,the targets become more and more complex.Imaging on complex media such as subsalt,small-scale,steeply dipping and surface topography structures brings a great challenge to imaging techniques.Therefore,the seismic imaging methods range from stacking-to migration-to inversion-based imaging,and the imaging accuracy is becoming increasingly high.This review paper includes:summarizing the development of the seismic imaging;overviewing the principles of three typical imaging methods,including common reflection surface(CRS)stack,migration-based Gaussian-beam migration(GBM)and reverse-time migration(RTM),and inversion-based least-squares reverse-time migration(LSRTM);analyzing the imaging capability of GBM,RTM and LSRTM to the special structures on three typical models and a land data set;outlooking the future perspectives of imaging methods.The main challenge of seismic imaging is to produce high-precision images for low-quality data,extremely deep reservoirs,and dual-complex structures.

Least-Squares Seismic Inversion with Stochastic Conjugate Gradient Method

With the development of computational power, there has been an increased focus on data-fitting related seismic inversion techniques for high fidelity seismic velocity model and image, such as full-waveform inversion and least squares migration. However, though more advanced than conventional methods, these data fitting methods can be very expensive in terms of computational cost. Recently, various techniques to optimize these data-fitting seismic inversion problems have been implemented to cater for the industrial need for much improved efficiency. In this study, we propose a general stochastic conjugate gradient method for these data-fitting related inverse problems. We first prescribe the basic theory of our method and then give synthetic examples. Our numerical experiments illustrate the potential of this method for large-size seismic inversion application.

2020国外石油物探技术进展与趋势

为了解2020年国外石油物探业务和技术的主要进展及发展趋势,对EAGE(欧洲地质学家与工程师学会)和SEG(国际勘探地球物理学家学会)年会与展会情况,及国外主要石油物探技术服务公司的业务发展与技术研发动态进行跟踪。2020年新冠肺炎疫情叠加低油价冲击,对石油物探技术服务需求和地震作业活动产生重大负面影响,尚未完全复苏的石油物探行业再次面临巨大挑战和压力。石油物探业务发展更加依赖轻资产业务,石油物探技术更加依赖低成本、高效、环保型技术。基于人工智能的地震数据处理解释技术快速发展,软件产品不断完善,基于云的石油物探综合数据管理方案是行业发展热点。

Reflection full waveform inversion

Because of the combination of optimization algorithms and full wave equations, full-waveform inversion(FWI) has become the frontier of the study of seismic exploration and is gradually becoming one of the essential tools for obtaining the Earth interior information. However, the application of conventional FWI to pure reflection data in the absence of a highly accurate starting velocity model is difficult. Compared to other types of seismic waves, reflections carry the information of the deep part of the subsurface. Reflection FWI, therefore, is able to improve the accuracy of imaging the Earth interior further. Here, we demonstrate a means of achieving this successfully by interleaving least-squares RTM with a version of reflection FWI in which the tomographic gradient that is required to update the background macro-model is separated from the reflectivity gradient using the Born approximation during forward modeling. This provides a good update to the macro-model. This approach is then followed by conventional FWI to obtain a final high-fidelity high-resolution result from a poor starting model using only reflection data.Further analysis reveals the high-resolution result is achieved due to a deconvolution imaging condition implicitly used by FWI.